Fused Location: The Contextual aspect of GPS

The underlying principle behind Contextually Aware applications is the process of constantly collecting data. One of the key aspects in this regard is the delivery of information by understanding the location of the user: a characteristic that is used by Google Now, in particular, to provide location based cards.

Contextual data on the basis of location services is quite an interesting thing, really. But location tracking on mobile devices, by default, have their own share of limitations.

Which is why Google introduced the concept of Fused Location services back in I/O 2013. It’s quite an efficient concept, and in this article I’ll be talking about how fused location services can be the game changer in contextual location tracking.

LOCATION SERVICES: BEFORE

The basic fundamentals of location based services can be summed up in three terms: Power Usage, Accuracy and Coverage. The methods of location tracking involved either GPS, WiFi, or even sensors. But, all three had their own share of pros and cons, as depicted in this slide off the I/O presentation.

(Data Source: Google Developers)

While either one of them would give a fair result in an outdoor situation, it would rely on the user travelling at a constant speed in some ways. Moreover, once the user transitions between indoor and outdoor environments, the readings – specially from GPS – go haywire.

Also, from a developer’s perspective, it was all a major headache. One had to keep in mind the various protocols involved, as depicted here.

(Data Source: Google Developers)

LOCATION SERVICES: NOW

With the advent of fused location services, the location tracking API has gotten a whole lot more intelligent. Fused location services brings in the best of sensor technology, GPS, cellular services and WiFi to create a comprehensive environment that will provide accurate data regarding location, while at the same time conserving power and maintaining a decent coverage level.

Again, this is nicely depicted in the following diagram from Google Devs.

(Data Source: Google Developers)

WHERE DO SENSORS COME IN?

Herein lies the most interesting facet to this entire conundrum. Sensors can actually understand and analyse data, to conserve power and at the same time provide valuable information to the user.

(Data Source: Google Developers)

For instance, if a person is in a theater, watching a movie. Conventional location services would constantly track his data, thereby utilising the GPS services continuously. However, what if we involved the accelerometer too, which would ask the GPS services to kick in only when the user moves? That would be much more efficient!

PROVIDING CONTEXTUAL DATA BASED ON LOCATION

Now one might ask: how do location services play their role in contextual applications?

Well, in a nutshell, location services and sensor data are the biggest contributing factors in generating contextual data. Information regarding a particular location – say when you’re in a particular city, or at a particular neighbourhood – can actually prove to be beneficial to the user.

Earlier, people would have to bring up their phones and type in the particular place of interest on the Maps applications or in the Search Engine, but with Contextual Location Awareness, the information can reach the user even before the user gives the command!

SOME STATS

Briefly, here are some statistics relevant to this article.

• Here is the statistic on the number of Foursquare check-ins till March 2015. The numbers have been growing, which emphasises on how important location services are.

(Data Source: Statista)

• Here is a statistic which shows the usage of geosocial and location based services in the U.S. from 2011 to 2013. During the 2013 survey period, 12 % of all US smartphone users used geosocial check-in services such as Foursquare, and 74 % used location based services.

(Data Source: Statista)

ROLE IN QUANTIFIED SELF

What if we could expand the usage of fused location based services? What if we could apply it to our wearables too, so that we can track health stats according to locations? For example, while running on a stretch of road that goes uphill, we could monitor our health stats. Health stats by themselves would just show increased heart rate, but combine it with the location data, and you’re presented with a cause-effect combination that makes Quantified data more conclusive!

I believe that location based services, aided with contextual machine learning and intelligent applications, can make our smartphones smarter and lead up to a more conducive and comprehensive experience overall.